Use profiler from skchange

Author: Tveten

interactive/explore_circular_binseg.py

@@ -1,11 +1,11 @@
 import plotly.express as px
-from streamchange.utils import Profiler
 
 from skchange.anomaly_detectors.circular_binseg import (
     CircularBinarySegmentation,
     make_anomaly_intervals,
 )
 from skchange.datasets.generate import generate_teeth_data
+from skchange.utils.benchmarking.profiler import Profiler
 
 df = generate_teeth_data(n_segments=3, mean=10, segment_length=20, p=1, random_state=7)
 detector = CircularBinarySegmentation(

interactive/explore_moscore.py

@@ -1,13 +1,13 @@
 import numpy as np
 import plotly.express as px
 from numba import njit
-from streamchange.utils import Profiler
 
 from skchange.change_detectors.moscore import Moscore, where
 from skchange.datasets.generate import add_linspace_outliers, generate_teeth_data
 from skchange.scores.mean_score import init_mean_score, mean_score
+from skchange.utils.benchmarking.profiler import Profiler
 
-# Compare skchange output to streamchange
+# Simple univariate example
 df = generate_teeth_data(n_segments=2, mean=10, segment_length=100, p=1, random_state=2)
 detector = Moscore()
 changepoints = detector.fit_predict(df)

interactive/explore_moscore_anomaly.py

@@ -1,10 +1,10 @@
 import numpy as np
 import plotly.express as px
-from streamchange.utils import Profiler
 
 from skchange.anomaly_detectors.moscore_anomaly import MoscoreAnomaly
 from skchange.datasets.generate import generate_anomalous_data
 from skchange.scores.mean_score import init_mean_score, mean_anomaly_score
+from skchange.utils.benchmarking.profiler import Profiler
 
 n = 500
 df = generate_anomalous_data(

interactive/explore_pelt.py

@@ -1,11 +1,11 @@
 import numpy as np
-from streamchange.utils import Profiler
 
 from skchange.change_detectors.pelt import Pelt
 from skchange.costs.mean_cost import init_mean_cost, mean_cost
 from skchange.datasets.generate import generate_teeth_data
+from skchange.utils.benchmarking.profiler import Profiler
 
-# Compare skchange output to streamchange
+# Simple univariate example
 df = generate_teeth_data(n_segments=2, mean=10, segment_length=100, p=1, random_state=2)
 detector = Pelt(min_segment_length=1)
 detector.fit_predict(df)

interactive/explore_seeded_binseg.py

@@ -1,8 +1,8 @@
 import plotly.express as px
-from streamchange.utils import Profiler
 
 from skchange.change_detectors.seeded_binseg import SeededBinarySegmentation
 from skchange.datasets.generate import generate_teeth_data
+from skchange.utils.benchmarking.profiler import Profiler
 
 df = generate_teeth_data(n_segments=2, mean=10, segment_length=20, p=1, random_state=7)
 detector = SeededBinarySegmentation(score="mean", growth_factor=2)